Neural Network Architecture Search with Differentiable Cartesian Genetic Programming for Regression
This work addresses the resource-intensive and poorly understood process of neural network architecture design for researchers and practitioners in deep learning, representing an incremental improvement over existing methods.
The paper tackles the challenge of designing effective neural network architectures by proposing a differentiable variant of Cartesian Genetic Programming (dCGPANN) combined with a memetic algorithm, resulting in evolved architectures that require less parameter space and achieve significantly lower error on regression tasks.
The ability to design complex neural network architectures which enable effective training by stochastic gradient descent has been the key for many achievements in the field of deep learning. However, developing such architectures remains a challenging and resourceintensive process full of trial-and-error iterations. All in all, the relation between the network topology and its ability to model the data remains poorly understood. We propose to encode neural networks with a differentiable variant of Cartesian Genetic Programming (dCGPANN) and present a memetic algorithm for architecture design: local searches with gradient descent learn the network parameters while evolutionary operators act on the dCGPANN genes shaping the network architecture towards faster learning. Studying a particular instance of such a learning scheme, we are able to improve the starting feed forward topology by learning how to rewire and prune links, adapt activation functions and introduce skip connections for chosen regression tasks. The evolved network architectures require less space for network parameters and reach, given the same amount of time, a significantly lower error on average.